The widespread image applications have greatly promoted the vision-based tasks, in which the Image Quality Assessment (IQA) technique has become an increasingly significant issue. For user enjoyment in multimedia systems, the IQA exploits image fidelity and aesthetics to characterize user experience; while for other tasks such as popular object recognition, there exists a low correlation between utilities and perceptions. In such cases, the fidelity-based and aesthetics-based IQA methods cannot be directly applied. To address this issue, this paper proposes a utility-oriented IQA in object recognition. In particular, we initialize our research in the scenario of underwater fish detection, which is a critical task that has not yet been perfectly addressed. Based on this task, we build an Underwater Image Utility Database (UIUD) and a learning-based Underwater Image Utility Measure (UIUM). Inspired by the top-down design of fidelity-based IQA, we exploit the deep models of object recognition and transfer their features to our UIUM. Experiments validate that the proposed transfer-learning-based UIUM achieves promising performance in the recognition task. We envision our research provides insights to bridge the researches of IQA and computer vision.

中文翻译：

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基于迁移学习的面向效用的水下图像质量评估

广泛的图像应用极大地促进了基于视觉的任务，其中图像质量评估（IQA）技术已成为一个越来越重要的问题。对于多媒体系统中的用户享受，IQA 利用图像保真度和美学来表征用户体验；而对于流行对象识别等其他任务，效用和感知之间的相关性较低。在这种情况下，不能直接应用基于保真度和基于美学的 IQA 方法。为了解决这个问题，本文提出了一种面向实用程序的对象识别 IQA。特别是，我们在水下鱼类检测的场景中启动了我们的研究，这是一项尚未完美解决的关键任务。基于这个任务，我们建立了一个水下图像效用数据库 (UIUD) 和一个基于学习的水下图像效用测量 (UIUM)。受基于保真度的 IQA 自上而下设计的启发，我们利用对象识别的深度模型并将其特征转移到我们的 UIUM。实验验证了所提出的基于迁移学习的 UIUM 在识别任务中取得了可喜的性能。我们设想我们的研究为连接 IQA 和计算机视觉的研究提供见解。